4G Security Using Physical Layer RF-DNA with DE-Optimized LFS Classification
| Autor: | Michael A. Temple, Paul K. Harmer, Mark A. Buckner, Ethan Farquhar |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Computer science
Orthogonal frequency-division multiplexing business.industry Real-time computing Physical layer Pattern recognition WiMAX symbols.namesake Gaussian function symbols Wireless Radio frequency Artificial intelligence Time domain Electrical and Electronic Engineering business Classifier (UML) |
| Zdroj: | Journal of Communications. 6 |
| ISSN: | 1796-2021 |
| DOI: | 10.4304/jcm.6.9.671-681 |
| Popis: | Wireless communication networks remain underattack with ill-intentioned “hackers” routinely gaining unauthorized access through Wireless Access Points(WAPs)–one of the most vulnerable points in an informationtechnology system. The goal here is to demonstrate thefeasibility of using Radio Frequency (RF) air monitoring to augment conventional bit-level security at WAPs. The specific networks of interest are those based on Orthogonal Frequency Division Multiplexing (OFDM), to include 802.11a/g WiFi and 4G 802.16 WiMAX. Proof-of-concept results are presented to demonstrate the effectiveness of a “Learningfrom Signals” (LFS) classifier with Gaussian kernel bandwidth parameters optimally determined through DifferentialEvolution (DE). The resultant DE-optimized LFS classifier is implemented within an RF “Distinct Native Attribute” (RFDNA) fingerprinting process using both Time Domain (TD) and Spectral Domain (SD) input features. The RF-DNA isused for intra-manufacturer (like-model devices from a given manufacturer) discrimination of IEEE compliant 802.11a WiFi devices and 802.16e WiMAX devices. A comparative performance assessment is provided using results from the proposed DE-optimized LFS classifier and a Bayesian-based Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) classifier as used in previous demonstrations. The assessment is performed using identical TD and SD fingerprint features for both classifiers. Finally, the impact of Gaussian, triangular, and uniform kernel functions on classifier performance is demonstrated. Preliminary resultsof the DE-optimized classifier are very promising, with correct classification improvement of 15% to 40% realized over the range of signal to noise ratios considered. |
| Databáze: | OpenAIRE |
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